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contacts: hassen.fourati@gipsa-lab.fr and daniel.simon@inria.fr

mode details here: http://necs.inrialpes.fr/pages/people-jobs/job-offers.php

The Unmanned Aerial Vehicles (UAVs) are aircrafts capable of flying and realizing a
mission without people on board. Originally developed as part of military activities,
there is now great potential for civilian activities (monitoring, mapping...). Many
constraints remain unresolved for the use of UAV in public space. Among the key aspects
to be tackled are embedded decision autonomy, the ability to perceive the environment
at all times, safety and dependability. These characteristics will in future be subject to
a certification process under development, but current UAVs still suffering from a lack
of robustness and autonomy.
The flight control systems (combining Inertial Measurement Unit, sensors, motors,
actuators...) provide stability and control functions and navigation of UAVs. For
example, an inertial unit, is necessary to calculate the attitude of the UAV in flight, is
often composed mainly of triads of MEMS (Micro-Electro-Mechanical Systems)
accelerometers, magnetometers and gyroscopes. These sensors are prone to defaults
(magnetic disturbances, biases...) which subsequently affects the control system (the
calculation of the attitude of the UAV and its stability in flight).
Many approaches for the attitude estimation are still unreliable and often drift over
time. Preliminary works have been developed in [1], [2] and possible improvements inthe strategies of robust estimation, combining inertial, magnetic data and / or GPS, are
still possible. On the other hand, the purely tele-operated control of an UAV, especially
in a cluttered environment is a delicate task that must be facilitated by the autonomous
execution of local actions such as for monitoring or avoidance of obstacles.
In this context, the proposed thesis can be developed in two parts with the following
- In the first part of the thesis, we will focus on the problem of attitude estimation
(3D spatial orientation) of the UAV. This information is often necessary for
navigation (stabilization of the UAV) in flight condition. The results of previous
works in literature at this level are encouraging but significant discrepancies are
still observed in the attitude estimation in case of sudden and accelerated
movements of the UAV [3]. To solve this problem, we propose new data fusion
approaches based on complementary filtering for the states estimation and
observation by combining inertial measurements (accelerometers and gyroscopes)
and magnetic measurements (magnetometers) and without resorting at each time
to GPS and velocity measurements.
The proposed methods until now for the attitude estimation in the case of UAVs
are based on the triad of sensors mentioned latter; we will search if it is possible
at a final step to overcome the gyro data and its intrinsic bias. In this case the
method will be reduced to the use of accelerometer and magnetometer.
- In the second part of the thesis, we will develop some fault-tolerant controls
(sensors and actuators defaults, but also real-time execution defaults and / or
loss of connection with the master station) using the sensors measurements and
the available execution resources.The approaches we propose will be based on the design of observers for the isolation and estimation of defaults, as well as the design and implementation of
robust control laws and flexible real-time scheduling.

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